This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We held a hands-on workshop devoted to "Analysis of ESR Data for Motional Dynamics" Workshop on 16-18 November 2007 at Cornell University, in Ithaca, NY. This ESR/EPR Data Analysis Software Workshop presented a cross-section of contemporary practical computational techniques and hands-on experience for participants who wanted to explore what advantages advanced ESR/EPR spectroscopy methods offer for studying biological and other systems. Topics included an introduction to theories underlying the spectral analysis, overview of software development strategies, input data preparation and software execution. Invited instructors who authored the software guided the attendees through hands-on practice with their software promoting discussion of current software needs. The typical attendee included those interested in learning the practical uses of contemporary ESR/EPR techniques to Biological or Other Systems, which require extensive use of Spectra Simulation in the analysis of the data. Sessions included the following presentations by ACERT and invited speakers: ACERT's Nonlinear Least-Squares Fitting of SRLS with Site Exchange package;Pattern-matching algorithms for automated symbolic processing of relaxation theory equations;Motional Dynamics by ESR and the Stochastic Liouville Equation;Can Molecular Dynamics Simulations Aid the Interpretation of ESR Spectra;Full Sc- Analysis of 2D-ELDOR;Supermatrix Operation Algorithms for Unified Analysis of Spectroscopy and Relaxation Data; HFHF ESR and Multifrequency Analysis; Integrated Approach to Modeling ESR Observables;EasySpin as a Tool for Spectral Simulations in EPR;Statistical Inference of Parameters from Least-Squares Fitting of Slow-Motional EPR Spectra.